Rating Evaluation Methods through Correlation

1. Rating Evaluation Methods through Correlation presented by Lena Marg, Language Tools Team @ MTE 2014, Workshop on Automatic and Manual Metrics for Operational Translation Evaluation The 9th edition of the Language Resources and Evaluation Conference, Reykjavik

4. Background on MT Programs @ MT programs vary with regard to: Scope Locales Maturity System Setup & Ownership MT Solution used Key Objective of using MT Final Quality Requirements Source Content

5. MT Quality Evaluation @ 1. Automatic Scores • Provided by the MT system (typically BLEU) • Provided by our internal scoring tool (range of metrics) 2. Human Evaluation • Adequacy, scores 1-5 • Fluency, scores 1-5 3. Productivity Tests • Post-Editing versus Human Translation in iOmegaT

6. The Database Objective: Establish correlations between these 3 evaluation approaches to • draw conclusions on predicting productivity gains • see how & when to use the different metrics best Contents: • Data from 2013 • Metrics(BLEU & PE Distance, Adequacy & Fluency, Productivity deltas) • Various locales, MT systems, content types • MT error analysis • Post-editing quality scores

7. Method Pearson’s r If r = +.70 or higher Very strong positive relationship +.40 to +.69 Strong positive relationship +.30 to +.39 Moderate positive relationship +.20 to +.29 Weak positive relationship +.01 to +.19 No or negligible relationship -.01 to -.19 No or negligible relationship -.20 to -.29 Weak negative relationship -.30 to -.39 Moderate negative relationship -.40 to -.69 Strong negative relationship -.70 or higher Very strong negative relationship

8. thedatabase Data Used 27 locales in total, with varying amounts of available data + 5 different MT systems (SMT & Hybrid)

9. correlationresults Adequacy vs Fluency A Pearson’s r of 0.82 across 182 test sets and 22 locales is a very strong, positive relationship • COMMENT • most locales show a strong correlation between their Fluency and Adequacy scores • high correlation is expected (with in-domain data customized MT systems) in that, if a segment is really not understandable, it is neither accurate nor fluent. If a segment is almost perfect, both would score very high • some evaluators might not differentiate enough between Adequacy & Fluency, falsely creating a higher correlation

10. correlationresults Adequacy and Fluency versus BLEU Fluency and BLEU across locales have a Pearson’s r of 0.41, a strong positive relationship Adequacy and BLEU across locales have a Pearson’s rof 0.26, a moderately positive relationship Adequacy, Fluency and BLEU correlation for locales with 4 or more test sets*

11. correlationresults Adequacy and Fluency versus PE Distance Fluency and PE distance across all locales have a cumulative Pearson’s r of -0.70, a very strong negative relationship Adequacy and PE distance across all locales have a cumulative Pearson’s r of -0.41, a strong negative relationship A negative correlation is desired: as Adequacy and Fluency scores increase, PE distance should decrease proportionally.

12. correlationresults Adequacy and Fluency versus Productivity Delta Productivity and Fluency across all locales with a cumulative Pearson’s r of 0.71, a very strong correlation Productivity and Adequacy across all locales with a cumulative Pearson’s r of 0.77, a very strong correlation

13. correlationresults Automatic Metrics versus Productivity Delta Productivity delta and BLEU with a cumulative Pearson’s r of 0.24, a weak positive relationship With a Pearson’s r of -0.436, as PE distance increases, indicating a greater effort from the post-editor, Productivity declines; it is a strong negative relationship

14. correlationresults Summary

15. takeaways CORRELATIONS The strongest correlations were found between: • Adequacy & Fluency • BLEU and PE Distance • Adequacy & Productivity Delta • Fluency & Productivity Delta • Fluency & PE Distance • The Human Evaluations come out as stronger indicators for potential post-editing productivity gains than Automatic metrics.

16. erroranalysis Data size: 117 evaluations x 25 segments (3125 segments), includes 22 locales, different MT systems (hybrid & SMT). • Taking this “broad sweep“ view, most errors logged by evaluators across all categories are: • Sentence structure (word order) • MT output too literal • Wrong terminology • Word form disagreements • Source term left untranslated

17. erroranalysis Similar picture when we focus on the 8 dominant language pairs that constituted the bulk of the evaluations in the dataset.

18. takeaways MOST FREQUENT ERRORS LOGGED • Across different MT systems, content types AND locales, 5 error categories stand out in particular. Questions: How (if) do these correlate to the post-editing effort and predicting productivity gains? How (if) can the findings on errors be used to improve the underlying systems? Are the current error categories what we need? Can the categories be improved for evaluators? Will these categories work for other post-editing scenarios (e.g. light PE)?

19. takeaways • Remodelling of Human Evaluation Form to: • increase user-friendliness • distinguish better between Ad & Fl errors • align with cognitive effort categories proposed in literature • improve relevance for system updates E.g.“Literal Translation“ seemed too broad and probably over-used.

20. nextsteps • focus on language groups and individual languages: do we see the same correlations? • focus on different MT systems • add categories to database (e.g. string length, post-editor experience) • add new data to database and repeat correlations • continuously tweak Human Evaluation template and process, as it proofs to provide valuable insights for predictions, as well as post-editor on-boarding / education and MT system improvement • investigate correlation with other AutoScores (…)

21. THANK YOU! lena.marg@welocalize.com with Laura Casanellas Luri, Elaine O’Curran, Andy Mallett